Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test

Huimin, Xing; Xu, Xingang; Li, Zhenhai; Chen, Yijin; Feng, Haikuan; Yang, Guijun; Chen, Zhaoxia

doi:10.1016/s2095-3119(16)61626-x

Cited by 39 publications

(26 citation statements)

References 34 publications

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“…Sensitivity analyses are used to evaluate the responses of model outputs to changes in model input parameters and to quantify the relative importance of those responses [35,36]. Many studies have been conducted to evaluate the sensitivity of plant parameters and the management practices of different crops under different models [24,[37][38][39][40][41][42][43]. Although the sensitivity assessment of soil is essential [25], limited attention has been paid to evaluating the sensitivity of soil parameters [42].…”

Section: Introductionmentioning

confidence: 99%

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

et al. 2020

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As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM-Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.Agronomy 2020, 10, 285 2 of 18 world, especially in tropical regions [1]. Pedotransfer functions (PTFs) are predictive functions used to estimate difficult-to-measure soil parameters with easily measurable soil parameters [2]. Point-based PTFs are used to determine soil parameters at specific, conventional values of matric potential [3]. The moisture content at −10 and −33 kPa (representing the field capacity (FC)) and moisture content at -1500 kPa (representing the permanent wilting point (PWP)) are the most common reference values for the soil moisture characteristic curve (SMCC) used for point PTFs [4].Point PTFs were developed [5] for Sri Lankan soil and confirmed the statistical accuracy of these PTFs for Sri Lankan conditions. However, these PTFs cannot perfectly calculate soil hydraulic parameters. In other words, there are always some errors that could be over or underestimated according to soil type. These errors may be different according to the application of the PTFs. Hence, the final estimation of the model quality should be defined by the model's applicability [6] because the estimation of hydraulic properties is not the...

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Section: Introductionmentioning

confidence: 99%

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

et al. 2020

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“…It is well known that the results of the sensitivity analysis studies depend on the boundary conditions chosen [66]. In this work, these conditions are climate datasets, actual data from both sites and the range of variation of the parameters [7,37,38]. The Morris method was used for initial screening of the parameters and to identify the influential and non-influential parameters.…”

Section: Elementary Effects Using the Morris Methodsmentioning

confidence: 99%

“…AOS parameters are reported in Table 1. The range of variation of the selected 54 parameters (reported in Table 1) was determined based on the AOS manual [57] and default values provided with the open-source AOS model database for the wheat crop and also on the previous studies of sensitivity analysis, carried out with the original FAO Aquacrop model [7,37,38]. Since there was no prior information on parameter frequency distributions, they were assumed to have an a priori uniform distribution.…”

Section: Setting Up the Parameters For Aos Modelmentioning

confidence: 99%

“…The main crop is winter wheat. Table 2 shows sowing and harvest dates used for model simulations at the different test sites [38], which were set on the basis of local agronomic practice. Soil information for both the test sites is presented in Table 3.…”

Section: Sites Descriptionmentioning

confidence: 99%

“…There have been studies of sensitivity analyses with the FAO developed Aquacrop model [7,37,38], but none yet on the open-source version AOS, which nevertheless lends itself more easily to the integration into regional-scale data assimilation schemes than the standard version. The studies of Aquacrop model SA was based on the arbitrarily chosen thresholds for yield output.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

et al. 2020

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The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model’s influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov–Smirnov statistic of 0.030 and lowest Anderson–Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov–Smirnov statistic of 0.122 and Anderson–Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

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The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study

Etedali

Adabi

Gorgin

et al. 2022

Stoch Environ Res Risk Assess

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Crop growth models are multi-outputs and can be valuable tools for the quanti cation of crop growth and production. However, these models usually require several input data, which are costly, time-consuming, and sometimes impossible to measure. These input parameters are mostly estimated by calibration and inverse solving. In this study, ve output variables of the AquaCrop model, including soil evaporation, crop transpiration, evapotranspiration, crop biomass at maturity, and grain yield, were investigated to study 47 genotypic input parameters on the output time series of the model for wheat in the Qazvin Synoptic Station. The SAFE toolbox in the Matlab environment was used to study the GSA and uncertainty of inputs and their impact on outputs. Applying the Monte-Carlo-based methods on closed-source models is not as easy as open-source models. The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin Synoptic Station over 36 years was analyzed using the Generalized Likelihood Uncertainty Estimation (GLUE) method.The uncertainty in the outputs of the validated AquaCrop model in simulating wheat yield in the Qazvin station over 36 years was analyzed using the GLUE method. Using RMSE<0.9 as the threshold in a 95% con dence level, the best parameter sets included all the observations. Results showed that evaporation and yield are the least reliable outputs of the AquaCrop model that have not been calibrated, while others consider them reliable. After that, the new domain of each output was determined based on the two indexes. Then we modi ed the domain to reduce its size. Finally, the probabilistic behavior of each input on outputs were introduced by the Easy Fit software. The signi cant result of this study is that the probabilistic nature of the input parameter that is calibrated for a particular output variable can differ from other output variables. Also, when we trust a speci c run of the model (calibrated run) as observed data, the uncertainty bounds covering are very high. Finally, we utilized the GLUE to optimize multi-output models by introducing one unique, optimized PDF for each input parameter for all outputs estimated by collecting all accepted output series of all target outputs.

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Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test

Cited by 39 publications

References 34 publications

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study

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